Gradient boosting MUST taggers for highly-boosted jets
Year: 2024
Type: article
Abstract: The Mass Unspecific Supervised Tagging (MUST) method has proven to be successful in implementing generic jet taggers capable of discriminating various signals over a wide range of jet masses. We implement the MUST concept by using eXtreme Gradient Boosting () classifiers instead of neural networks (NNs) as previously done. We build both fully-generic and specific multi-pronged taggers, to identify 2, 3, and/or 4-pronged signals from SM QCD background. We show that -based taggers are not only easier to optimize and much faster than those based in NNs, but also show quite similar performance, even when testing with signals not used in training. Therefore, they provide a quite efficient alternative machine-learning implementation for generic jet taggers.
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,790
Awards: Centro de Física Teórica de Particulas, G1578237752, G2262748287, G2430681607, Fenomenologia de Neutrinos: das Oscilações às Ondas Gravitacionais, G282847690, G3480869486, G366626502, G388309941, G425668148, G4959308268, G5467123088, G549088696, G6496269175, G7084143925, Centro de Física Teórica de Particulas, G8684520763
OpenAlex ID: W4404657106